An optimization algorithm design based on chaotic variable is proposed for multilayer fuzzy neural network.
提出了一种基于混沌变量的多层模糊神经网络优化算法设计。
The Chaotic neural network model can be used to solve many multi-dimensioned, discrete, non-convex, nonlinear constrained optimization problems.
基于混沌神经网络模型可以有效地解决高维、离散、非凸的非线性约束优化问题。
Combining grading method with chaotic optimization, the neural network model achieves rapid training and avoids local minimum when there are a lot of samples to be trained.
考虑神经网络在训练大规模样品时易陷入局部极小，用梯度下降法与混沌优化方法相结合，使神经网络实现快速训练的同时，避免陷入局部极小。
In the third part, the prediction model of fuzzy optimal selection neural network based on chaotic optimization algorithm is studied.
第三部分对基于混沌优化算法的模糊优选神经网络预测模型进行研究。
This paper studied the chaotic neural network and applied it to a typical combinatorial optimization problem and broadband matching network design.
本文以混沌神经网络为主要研究对象，并应用于典型组合优化问题求解和宽带匹配网络设计之中。
In this paper, the optimization design for self-adaptive control system of feed-forward neural network is proposed based on chaotic variable.
基于混沌变量，提出一种神经网络自适应控制系统的优化设计方案。
In this paper, the optimization design for self-adaptive control system of feed-forward neural network is proposed based on chaotic variable.
基于混沌变量，提出一种神经网络自适应控制系统的优化设计方案。
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